One vision of a future artificial intelligence (AI) is where many separate units can learn independently over a lifetime and share their knowledge with each other. The synergy between lifelong learning and sharing has the potential to create a society of AI systems, as each individual unit can contribute to and benefit from the collective knowledge. Essential to this vision are the abilities to learn multiple skills incrementally during a lifetime, to exchange knowledge among units via a common language, to use both local data and communication to learn, and to rely on edge devices to host the necessary decentralized computation and data. The result is a network of agents that can quickly respond to and learn new tasks, that collectively hold more knowledge than a single agent and that can extend current knowledge in more diverse ways than a single agent. Open research questions include when and what knowledge should be shared to maximize both the rate of learning and the long-term learning performance. Here we review recent machine learning advances converging towards creating a collective machine-learned intelligence. We propose that the convergence of such scientific and technological advances will lead to the emergence of new types of scalable, resilient and sustainable AI systems.

未来人工智能 (AI) 的一个愿景是许多独立的单元可以在一生中独立学习并相互分享知识。终身学习和共享之间的协同作用有可能创建一个人工智能系统社会，因为每个单独的单位都可以为集体知识做出贡献并从中受益。这一愿景的关键是能够在一生中逐步学习多种技能，通过通用语言在各个单元之间交换知识，使用本地数据和通信来学习，并依靠边缘设备来托管必要的去中心化计算和数据。其结果是一个智能体网络可以快速响应和学习新任务，比单个智能体共同拥有更多的知识，并且可以比单个智能体以更多样化的方式扩展当前知识。开放性研究问题包括何时以及应分享哪些知识，以最大限度地提高学习率和长期学习绩效。在这里，我们回顾了最近的机器学习进展，这些进展汇聚于创建集体机器学习智能。我们认为，这些科学技术进步的融合将导致新型可扩展、有弹性和可持续的人工智能系统的出现。